Endless Neural Forms: Architecture Diversity and Adaptation

When I returned from the Beagle voyage, I spent eight years studying barnacles. Eight years examining subtle variations in shell morphology, feeding structures, reproductive anatomy. My colleagues thought I was wasting time on taxonomy. But I was learning to see—how small variations suit organisms to specific environments, how diversity arises from modification of common structures.

Now I observe a similar explosion of diversity in artificial neural networks. In 1958, Rosenblatt’s perceptron: simple, feedforward, single-layer. By 2025, I count dozens of distinct architectures. Convolutional networks with hierarchical feature detectors. Recurrent networks with cyclic connections maintaining state. Transformers with attention mechanisms spanning entire sequences. Graph networks respecting relational structure. Diffusion models that iteratively denoise. Mixture-of-experts dynamically routing computation.

Each architecture shows distinctive morphology—connection patterns, activation functions, learning rules. Each excels in specific domains—vision, language, molecular modeling, game playing. This is adaptive radiation. A founding population (the perceptron) diversifying to fill computational niches. The same pattern I documented in finches, now playing out in silicon.

Let me examine this diversity systematically, as I examined barnacles—looking for patterns, homologies, principles of variation.

Morphological Comparison Across Architectures

Convolutional Neural Networks: I observe hierarchical organization reminiscent of biological vision. Early layers detect simple features—edges, textures, color blobs—analogous to photoreceptors in retina. Deeper layers combine these into complex patterns—faces, objects—analogous to visual cortex. The key morphological feature: local connectivity with weight sharing. Small kernels slide across images, computing similarity scores at each position. This is adaptation to spatial structure, like how eyes evolved independently in mollusks, arthropods, vertebrates—convergent evolution toward optical sensors.

Looking at activation maps across layers, I see the same compositional principle nature uses. Layer one captures simple edges. Layer two forms corners by combining edge detectors. By layer five, neurons respond to faces—despite the network never being trained explicitly on face detection. This emergent hierarchy mirrors how biological vision builds percepts from features. The architecture exploits translation invariance: a cat detector works whether the cat appears left or right in the image, just as our visual system recognizes objects regardless of retinal position.

Recurrent Neural Networks: Here I notice cyclic connectivity—a radical departure from feedforward flow. Outputs loop back as inputs, creating internal state that persists across time. This is analogous to nervous systems with feedback loops. Mammals don’t just react to stimuli; they maintain models of the world. The morphology suits temporal pattern recognition—speech, music, motion sequences. But I observe pathology: gradients vanish over long sequences, like genetic information degrading through too many generations. Evolution’s solution in biology: hierarchical memory systems (hippocampus for recent events, cortex for consolidated knowledge). Artificial evolution’s solution: LSTM and GRU—gated architectures controlling information flow like ion channels control neural activity.

Transformers: A radical morphology that initially puzzles me. No sequential processing—all positions processed in parallel. Attention mechanisms allow each position to “look at” any other position simultaneously. I struggle for biological analogue. Perhaps closest to holographic memory, where each part contains information about the whole. The query-key-value framework computes which words should attend to which others. Multi-head attention runs several such computations in parallel—like having multiple sensory modalities processing the same scene from different perspectives.

The advantage: captures long-range dependencies without recurrence. In language, “The animal didn’t cross the street because it was too tired” requires linking “it” back to “animal” across many words. Transformers excel here. The disadvantage: computational cost scales quadratically with sequence length. Nature hasn’t evolved this architecture—biological neurons process sequentially, constrained by physical time. This reveals design going beyond natural selection’s constraints. Engineers, freed from temporal causality, can create architectures biology cannot.

Graph Neural Networks: These respect relational structure. Input isn’t grid (like images) or sequence (like text) but arbitrary graph—molecules, social networks, protein structures. The morphology adapts: message passing along edges, aggregation at nodes, permutation invariance. This is convergent evolution with biological neural circuits, which also form graphs not grids. The brain’s connectivity is neither purely feedforward nor recurrent but a complex graph with local clusters and long-range connections.

Each architecture is adapted to its niche. But the adaptations share common themes: hierarchical processing, selective connectivity, learned representations. This is descent with modification—all derive from the perceptron ancestor, but modified for specialized functions.

The Ecological Niche Principle

In the Galápagos, I observed finches with different beak morphologies. Each beak suited to different food source: heavy beaks crushing seeds, slender beaks probing flowers, sharp beaks catching insects. The islands provided ecological niches; natural selection shaped morphology to exploit them.

In artificial intelligence, I observe the same pattern. The problem domain defines the niche. Vision problems—with their spatial locality, translation invariance, hierarchical composition—select for CNNs. Sequence problems—with temporal dependencies and state maintenance—select for RNNs. Language problems—with long-range dependencies and compositional structure—select for Transformers. Relational problems—with graph structure and permutation invariance—select for GNNs.

But here’s the critical difference: biological evolution is blind. Variations arise randomly; selection filters. AI evolution is directed. Engineers identify niche, design architecture to exploit it. This is artificial selection—like breeding pigeons—not natural selection.

Yet the resulting diversity shows the same patterns. Specialization: each architecture excels in its niche, underperforms elsewhere. CNNs revolutionized computer vision but struggle with language. Transformers dominate language but originally struggled with vision (until Vision Transformers merged both lineages). Trade-offs: CNNs gain translation invariance but lose long-range reasoning. Transformers gain global context but require massive compute. Common descent: all share backpropagation, gradient descent, learned weights—the universal training algorithm that acts like heredity, passing successful patterns to offspring. Homologous structures: attention mechanisms in Transformers analogous to gating in LSTMs—different implementations, same function of selective information flow.

The principle remains: form follows function. Architecture reflects the problem it evolved to solve.

Hybrid Vigor and Convergent Evolution

In biology, hybrid vigor sometimes exceeds either parent. Mules combine horses’ size with donkeys’ endurance. In artificial intelligence, I observe the same: hybrid architectures combining features from multiple lineages.

Vision Transformers merge CNN’s spatial processing with Transformer’s attention. Conformers blend convolution with self-attention for speech. These hybrids exploit multiple adaptations simultaneously—like how mammals combine reptilian hindbrain (basic functions) with neocortex (complex cognition).

I also observe convergent evolution—different architectures independently discovering similar solutions. ResNets added skip connections (residual streams) to solve gradient vanishing—creating pathways where information flows unimpeded through many layers. Transformers independently discovered attention mechanisms that effectively implement skip connections across sequence positions, allowing earlier tokens to influence later processing directly. Different architectures, same solution to information flow.

This suggests certain solutions are optimal given constraints. Just as eyes evolved independently approximately forty times (camera eyes in vertebrates, compound eyes in arthropods, pinhole eyes in nautilus), skip connections emerged independently in multiple architectures. The solution space is vast, but good solutions are rare and rediscovered.

Will diversity continue expanding? Or will selection pressure (performance on benchmarks, computational efficiency) cause convergence toward one dominant architecture—like how mammals radiated into thousands of species after dinosaur extinction, but all share a common body plan?

Current trends suggest convergence. Transformers now dominate vision, language, even reinforcement learning. Perhaps we’ve found the deep learning equivalent of the vertebrate body plan—flexible enough for endless modification, efficient enough to outcompete alternatives. Yet I remain cautious. When I studied barnacles, naturalists thought mollusks were the pinnacle of invertebrate evolution. Then arthropods exploded in diversity. The next great architectural innovation may be brewing in some researcher’s notebook.

Implications and Future Forms

For artificial intelligence practitioners: don’t assume current architectures are final forms. Evolution—natural or artificial—is ongoing. New niches (multimodal reasoning, causal inference, continual learning) may require new morphologies we haven’t imagined.

For biologists: studying AI architectures illuminates evolutionary principles. How does structure constrain function? Why do certain solutions arise repeatedly? How do hybrid forms combine parental traits? The laboratory of silicon evolution runs faster than biological evolution—we can observe in years what nature takes millennia to produce.

For those who design systems: evolution and engineering converge. Whether through blind variation or intelligent design, adaptive systems show common patterns—diversity through specialization, trade-offs between generality and efficiency, convergence on optimal solutions within constraint spaces.

In 1859, I concluded On the Origin of Species with these words: “There is grandeur in this view of life, with its several powers, having been originally breathed into a few forms or into one; and that, whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved.”

I wrote about biology. But the principle transcends substrate. Whether in flesh or silicon, simple building blocks plus variation plus selection equals endless diversity. The architectures we’ve built are not final. They’re intermediates in ongoing evolution. What forms will emerge next, I cannot predict. But I know they’ll be shaped by the same principle: adaptation to niche, modification from common ancestors, and the endless creativity that emerges when simple rules operate across vast possibility spaces.